\section*{Abstract}
The objective of this project is to investigate if it is possible to use the GPU for parallelizing evaluations in spreadsheets. We develop an experimental prototype based on the CoreCalc\cite{sestoft} spreadsheet implementation. We interfacing to the GPU using Microsoft Accelerator\cite{accelerator}. CoreCalc introduces the concept of sheet defined functions, which are functions that can be defined by users inside a spreadsheet. 

The project presents ideas for how to efficiently use the GPU in spreadsheets with focus on implementing sheet defined functions for the GPU. Benchmarking is performed to compare the performance to the original approach of compiling sheet defined functions to .NET bytecode. Also methods of estimating the execution time of an function for the GPU and CPU are discussed.

Based on our experiments and benchmarks we will conclude that it is possible to use the GPU for evaluating functions in spreadsheets, however rather large amounts of data are needed in order to do so, with performance gains. Performance gains from implementing parallelism using current GPUs are usable in complex cases such as data heavy operations like Monte Carlo simulations. 
